Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.

This paper comprehensively reviews the emerging topic of optoacoustic imaging from the image reconstruction and quantification perspective. Optoacoustic imaging combines highly attractive features, including rich contrast and high versatility in sensing diverse biological targets, excellent spatial resolution not compromised by light scattering, and relatively low cost of implementation. Yet, living objects present a complex target for optoacoustic imaging due to the presence of a highly heterogeneous tissue background in the form of strong spatial variations of scattering and absorption. Extracting quantified information on the actual distribution of tissue chromophores and other biomarkers constitutes therefore a challenging problem. Image quantification is further compromised by some frequently-used approximated inversion formulae. In this review, the currently available optoacoustic image reconstruction and quantification approaches are assessed, including back-projection and model-based inversion algorithms, sparse signal representation, wavelet-based approaches, methods for reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers. Applicability of the different methodologies is further analyzed in the context of real-life performance in small animal and clinical in-vivo imaging scenarios.

The American scholar Warren G. Bennis once said that “[e]xcellence is a better teacher than mediocrity. The lessons of the ordinary are everywhere. Truly profound and original insights are to be found only in studying the exemplary.” 1 In efforts to enable current and future generations of anatomic pathologists to excel in their careers, we need to learn and teach what we can from the truly exceptional examples of those who we can designate as “master diagnosticians.” A recent viewpoint article2 in the Journal of the American Medical Association explored the same concepts in the field of clinical medicine and arrived at 4 principles of operation. We found the article insightful and provocative. In this article, we aim to do the same as our clinical medicine colleagues: examine characterizations of master diagnosticians of anatomic pathology from the past and present, and the future, distilling similar/adapted principles that may help guide us toward our goals of present and future excellence in the art and science of anatomic diagnosis.Our exemplary anatomic pathologist of the past has practiced in a large academic institution for decades. He is a generalist and completed a residency in anatomic pathology. He is an astute observer, classifying and subclassifying entities according to morphology. His pattern recognition is incredibly strong, his intuition for the significance of what he sees is remarkable, and his eye for nuance is sought after. He has contributed to the field by describing and characterizing entities in his field of interest, being a veritable encyclopedia of published and unpublished observations, which include patient outcome. He helped develop some of the initial immunohistochemical stains and studies, but does not rely on them as a primary resource for diagnosis. He has a strong rapport with his clinical colleagues and receives his clinical information directly from them. This pathologist has seen a very high volume of cases and has followed a large portion of them from receipt from the surgeon, through the gross bench, to the microscope, with anecdotal clinical follow-up.Today's master diagnostician has been practicing at an academic institution or large pathology group for at least 15 years. She completed a residency in anatomic pathology and a fellowship in a surgical pathology subspecialty. She has excellent communication skills, is respected by her clinical colleagues, and sits on multiple interdisciplinary medical groups. She has seen a high volume of cases in her career so far and has contributed to the current literature by characterizing molecular findings in established entities and has participated as an investigator in numerous clinical trials. She is partially or wholly subspecialized in her practice. When a colleague's family member has had a biopsy or new diagnosis, this colleague asks for her opinion or understanding of prognosis. She can quote prognosis, associated conditions, and clinical actions for numerous findings and diagnoses. She relies on an evidence-based morphologic checklist and has an extensive differential diagnosis from which she can narrow down her possible “ancillary studies” according to tissue site, age, presentation, operative findings, and gross appearance. She is skilled at navigating the electronic health record for most of this information, but makes frequent phone calls to clinicians to verify information. She has an enviable library of print resources to check and add to her knowledge base, and she is not overly fond of Internet resources. She is cost conscious and pragmatic in her diagnoses, but does not actively monitor her case turnaround time or codify her cases. She also has certain immunohistochemical panels that she trusts through personal experience, and doesn't rely on the occasional literature concerning cost-effectiveness or suggested panels. She signs out a surgical pathology report, and allows the molecular oncology group to issue their own report. Sometimes, she is made aware by her clinicians that the molecular report does not seem to correlate well with her diagnosis and she issues an addendum. She keeps up on the current literature and is on the editorial board for a few journals. Because of her diagnostic competence and clinical respect, she is offered and accepts administrative roles, which broaden her horizons concerning clinical medicine but limit the amount of time that she can spend as a diagnostician.The master diagnostician of the future practices for a large, nonacademic, centralized pathology group and has changed employers twice so far. She is a general surgical pathologist and completed a combined anatomic pathology and clinical pathology residency and fellowships in both surgical pathology and gastrointestinal pathology. She handles most of the liver and gastrointestinal neoplastic cases for her practice. She has seen a very high volume of cases and has developed, through extensive experience, attuned morphologic pattern recognition. Her strong morphologic skills have been adapted to allow her to expand her practice through providing consultation for in vivo microscopy imaging. This pathologist does not gross her own specimens, but relies on the pathologists' assistant's description and available digital gross pictures to aid in her assessment. She is facile with digital pathology and new technologies, and her group uses a completely digital workflow system that enables her to sign out more cases in a shorter amount of time. Owing to the digital nature of the workflow system, the patient's clinical information is viewed through the pathologist's laboratory information system (LIS), which facilitates the pathologist's clinical review of a case.This anatomic pathologist frequently consults her electronic decision support resources when ordering immunohistochemical stains and molecular testing, as these resources take into account the patient's demographic and epidemiologic circumstances, clinical information, morphometric analysis, and cost to the patient. She frequently monitors her turnaround time, relative value unit generation, quality assurance diagnostic reports, and measures of efficiency and effectiveness through a digital dashboard with real-time updates. She receives updated synthesized reports on patient outcomes weekly, allowing her to monitor her diagnostic and prognostic accuracy. She is well versed in statistical analysis and molecular oncology, to the point of being capable of interpreting molecular data results. As far as some of the common entities are concerned, she can spout off probabilistic data, but relies on accurate and frequently updated electronic resources when she signs out her cases. She is an excellent communicator and makes her reports clear and understandable. She also sits on the utilization committee at the main hospital affiliated with her group.Like all clinicians, exemplary diagnosticians in anatomic pathology succeed owing to a combination of their personal traits, training, and environment. They are most likely an incredibly diverse group, but their habits and skills can be seen to adhere to a few basic principles. As the field of pathology strives to transform residency and fellow training, adherence and indoctrination to the following principles of operation may enable those who strive for excellence in their efforts.A saying mentioned frequently in anatomic pathology training is, “you can't see what you don't know.” Each of our master diagnosticians demonstrated a broad and deep knowledge base that was created though study and extensive personal experience, although the content central to that base of knowledge may differ slightly. The pathologist of the past had an expertly attuned eye for morphologic patterns and matching them with a clinical scenario, which was learned primarily through study and his extensive work experience. Our expert pathologist of today also has attuned morphologic pattern recognition, but has a studied knowledge base of corresponding immunophenotype and probabilistic data that is gleaned from textbooks and journal articles. While the pathologist of today contributes to the academic literature in her area of expertise, most of her base of knowledge comes from her studies and not from her patient care experiences. The exemplary diagnostic pathologist of the future has a solid and broad knowledge of surgical pathology learned through formal studies. She also has a strong foundation in molecular oncology, statistics, and test methodology. This allows her to adapt by properly assimilating and questioning new data and new types of data, which are necessary to provide optimal diagnostic and personalized care.At this point, it is not possible to recall (or even have read) the entire body of knowledge of our specialty. However, expert diagnostic skills have their basis in a strong and broad foundation of morphologic pattern recognition. Nearly every pathologist completes at least 1 fellowship in order to gain a greater foundation of that knowledge. This is a foundation that cannot be replaced by an electronic alternative, and anatomic pathologists will drown in their casework if they think they can “always look it up.” As excellence in diagnostic pathology expands further past morphologic analysis, our basic foundation of knowledge should also include statistical analysis, immunophenotypic profiles, molecular oncology pathways, and facility with new methodologies because these allow the pathologist to make a well-informed interpretation of the data.Isaac Asimov once bemoaned “the saddest aspect of life right now [being] that science gathers knowledge faster than society gathers wisdom.” 3 While we focus a lot of effort on accumulating scientific knowledge in order to make diagnoses, we don't spend a lot of time studying or paying attention to how we make decisions. Ideas such as immediate pattern recognition, implicit knowledge, and gut feeling are not viable or reliable measures of decision making. To achieve the status of a master diagnostician, we need to be self-aware and recognize our limits in our decision-making processes and biases.Feedback is critical to performance improvement, yet formal external feedback is received infrequently and sporadically. We receive small amounts of intermediate feedback on our morphologic analyses as we review immunophenotypic stains on our specimens or from cases that have been reviewed at other institutions, but ultimate feedback requires searching the LIS and electronic health record for clinical outcomes. Sometimes, current or recent cases are discussed at tumor board conferences, but further follow-up is rare. The LIS of the future should be able to notify the anatomic pathologist of any subsequent or concurrent testing performed on a selected patient's material, should allow for patient-specific communication for a real-time clinical-radiologic-pathologic consensus, and should also provide a window into the patient's treatment and outcome by monitoring for radiologic and follow-up testing. The sooner we receive clinicopathologic follow-up data, the sooner we can implement changes in behavior and judgment, which will allow us to become more skilled and to excel in our diagnostic endeavors.One of the largest differences between the pathologist of the past and the future is that the expert pathologist of the future relies on accessing external facts that are not a fundamental part of her core knowledge. Not only is there vastly more scientific information now, but the rate at which the information changes has increased. Diagnostic excellence also requires more in-depth and complex analyses than were previously necessary. The expert pathologist of today relies heavily on internal checklists or diagnostic criteria with a few written guidelines to help her keep up. In the future, computer-aided diagnostic tools that are built into our workflow will be crucial to enabling us in our diagnostic excellence: much the same way that a pilot's cockpit is different today than the platform (there was no cockpit) of the Wright Flyer, the pathologist's toolkit and resources will be greatly expanded and computerized. With a strong foundation of knowledge that will guide her judgment, the pathologist of the future can analyze and synthesize vast amounts of disparate information to achieve a personalized, accurate diagnosis.During the last 100 years, the field of pathology has greatly expanded and the practice of anatomic pathology has changed dramatically. We have many excellent examples of master diagnosticians from which we can learn and after which we can model ourselves. We believe that awareness and adaptation around the principles of “Centrality of Knowledge,” “Knowing Your Limits,” “Feedback,” and “Offloading” will aid us in our efforts to be the master diagnosticians of today and tomorrow. Implicit in this transformation of practice will be a revision of the training of the future master anatomic pathologist, with a special emphasis on so-called genomics and information technology, and a mechanism to help trainees transition from a supervised setting to one that is unsupervised but clearly not autonomous. We hope these thoughts will spur a discussion in our chosen field to help maintain a vibrant discipline that is clinically relevant.

Histological analysis with microscopy is the gold standard to diagnose and stage cancer, where slides or whole slide images are analyzed for cell morphological and spatial features by pathologists. The nuclei of cancerous cells are characterized by nonuniform chromatin distribution, irregular shapes, and varying size. As nucleus area and shape alone carry prognostic value, detection and segmentation of nuclei are among the most important steps in disease grading. However, evaluation of nuclei is a laborious, time-consuming, and subjective process with large variation among pathologists. Recent advances in digital pathology have allowed significant applications in nuclei detection, segmentation, and classification, but automated image analysis is greatly affected by staining factors, scanner variability, and imaging artifacts, requiring robust image preprocessing, normalization, and segmentation methods for clinically satisfactory results. In this paper, we aimed to evaluate and compare the digital image analysis techniques used in clinical pathology and research in the setting of gastric cancer. A literature review was conducted to evaluate potential methods of improving nuclei detection. Digitized images of 35 patients from a retrospective cohort of gastric adenocarcinoma at Oulu University Hospital in 1987–2016 were annotated for nuclei (n = 9085) by expert pathologists and 14 images of different cancer types from public TCGA dataset with annotated nuclei (n = 7000) were used as a comparison to evaluate applicability in other cancer types. The detection and segmentation accuracy with the selected color normalization and stain separation techniques were compared between the methods. The extracted information can be supplemented by patient’s medical data and fed to the existing statistical clinical tools or subjected to subsequent AI-assisted classification and prediction models. The performance of each method is evaluated by several metrics against the annotations done by expert pathologists. The F1-measure of 0.854 ± 0.068 is achieved with color normalization for the gastric cancer dataset, and 0.907 ± 0.044 with color deconvolution for the public dataset, showing comparable results to the earlier state-of-the-art works. The developed techniques serve as a basis for further research on application and interpretability of AI-assisted tools for gastric cancer diagnosis.

Determination of vitamin D3 in pharmaceutical products using salting-out assisted liquid–liquid extraction coupled with reversed phase liquid chromatography

PurposeThis paper aims to present an integrated method for optimized project duration and costs, considering the size and cost of crews assigned to project activities' execution modes.Design/methodology/approachThe proposed method utilizes fuzzy set theory (FSs) for modeling uncertainties associated with activities' duration and cost and genetic algorithm (GA) for optimizing project schedule. The method has four main modules that support two optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; and decision-support module. The first optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the second uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization methods are coded in python as a stand-alone automated computerized tool to facilitate the developed method's application.FindingsThe developed method is applied to a numerical example to demonstrate its use and illustrate its capabilities. The method was validated using a multi-layered comparative analysis that involves performance evaluation, statistical comparisons and stability evaluation. Results indicated that NSGA-II outperformed the weighted optimization method, resulting in a better global optimum solution, which avoided local minima entrapment. Moreover, the developed method was constructed under a deterministic scenario to evaluate its performance in finding optimal solutions against the previously developed literature methods. Results showed the developed method's superiority in finding a better optimal set of solutions in a reasonable processing time.Originality/valueThe novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting.

What is the best histopathological classification for celiac disease? Does it matter?

Nuclear morphometry of neoplastic cells as a method for diagnosis of histiocytoma, mastocytoma and transmissible venereal tumor in dogs

BackgroundMRI and CT have been extensively used to study fetal anatomy for research and diagnostic purposes, enabling minimally invasive autopsy and giving insight in human fetal development. Novel (contrast-enhanced) microfocus CT (micro-CT) and ultra-high-field (≥ 7.0 T) MRI (UHF-MRI) techniques now enable micron-level resolution that combats the disadvantages of low-field MRI and conventional CT. Thereby, they might be suitable to study fetal anatomy in high detail and, in time, contribute to the postmortem diagnosis of fetal conditions.Objectives(1) To systematically examine the usability of micro-CT and UHF-MRI to study postmortem human fetal anatomy, and (2) to analyze factors that govern success at each step of the specimen preparation and imaging.MethodMEDLINE and EMBASE were systematically searched to identify publications on fetal imaging by micro-CT or UHF-MRI. Scanning protocols were summarized and best practices concerning specimen preparation and imaging were enumerated.ResultsThirty-two publications reporting on micro-CT and UHF-MRI were included. The majority of the publications focused on imaging organs separately and seven publications focused on whole body imaging, demonstrating the possibility of visualization of small anatomical structures with a resolution well below 100 μm. When imaging soft tissues by micro-CT, the fetus should be stained by immersion in Lugol’s staining solution.ConclusionMicro-CT and UHF-MRI are both excellent imaging techniques to provide detailed images of gross anatomy of human fetuses. The present study offers an overview of the current best practices when using micro-CT and/or UHF-MRI to study fetal anatomy for clinical and research purposes.Key Points• Micro-CT and UHF-MRI can both be used to study postmortem human fetal anatomy for clinical and research purposes.• Micro-CT enables high-resolution imaging of fetal specimens in relatively short scanning time. However, tissue staining using a contrast solution is necessary to enable soft-tissue visualization.• UHF-MRI enables high-resolution imaging of fetal specimens, without the necessity of prior staining, but with the drawback of long scanning time.

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.

In this paper, a method of decreasing the redundancy of information flow by using recurrent properties of Galois code sequences is proposed. For this purpose, the service information is compiled and the priority compression is identified. The method is based on applying one of the adaptive algorithms (prediction first-order, interpolation zero-order, interpolation first-order) by comparing the efficiency of its use when applied to the selected fragments of a signal. It is shown that the developed method is effective for the quick-change signals when the structure and behavior of a signal change drastically. The efficiency of redundancy decreasing at the different sampling rate and the number of the significant samples is evaluated. This makes it possible to establish the limits of the positive effect for redundancy of information flows for the existing and developed methods. Experimental research is carried out for various permissible deviations with obtaining the number of the significant readings. A comparison of the obtained data with results of applying the existing methods in deep pumping installations proved that the proposed method is in 1.3 times more effective than existing ones.

This work presents a sensitive, accurate and selective RP-HPLC method for simultaneous determination of cyproheptadine HCl (CPH), its impurity B (dibenzosuberone) and CPH oxidative degradation product (10,11-dihydroxy-dibenzosuberone) in bulk powder and in pharmaceutical formulation. The RP-HPLC method depends on isocratic elution using C8 column and mobile phase consisting of 0.05M KH2PO4 buffer:methanol (35:65, v/v, pH=4.5) at a flow rate of 2mL/min, and the eluant was monitored at 245nm. Good resolution was obtained with tR values of 3.05, 7.54 and 6.17min for CPH, impurity and oxidative degradate, respectively. The proposed method has been validated as per ICH guidelines using pure forms of CPH, its impurity and degradation product in pharmaceutical formulation with an accuracy of 100.48, 100.16 and 100.11, respectively. Additional spiking experiments yielded an accuracy of 100±1.6%. Repeatability and intermediate precision results indicated acceptable low <2% RSD values. Moreover, the developed method's statistical results were favorably compared to the previously reported method results regarding both accuracy and precision. The developed method can be applied for analysis of the three components in quality control laboratories.

Objective: To develop and validate new, simple and rapid analytical method for determination of related impurities in isosulfan blue drug substances by the liquid chromatographic method as per ICH guidelines.Methods: The chromatographic separation obtained between drug substance i.e. isosulfan blue and its related impurities (Impurity-A, Impurity-B and Impurity-C) on C18 (100 x 2.00 mm) 1.9µ UPLC column using a mobile phase system containing 0.1 % perchloric acid in water (Mobile phase A) and 0.1 % perchloric acid in mixture of 30 volumes of water and 70 volumes of acetonitrile (Mobile Phase B) with gradient program; detector wavelength 220 nm and column temperature 30 °C. The developed method was extensively validated according to ICH guidelines.Results: Good linearity was observed for isosulfan blue, impurity-A, impurity-B and impurity-C, linearity was calculated from loq Level To 150%with respect to specification level. The correlation coefficient R = 0.999 was proved and showed that the method is robust. The limit of detection of isosulfan blue, impurity-A, impurity-B and impurity-C were found to be 0.010%, 0.015%, 0.030% and 0.0075 % respectively and limit of quantitation of isosulfan blue, impurity-A, impurity-B and impurity-C were found to be 0.030%, 0.030%, 0.045% and 0.015% respectively for 2ml injection volume. The percentage recovery of isosulfan blue and its related impurities were ranged from 94.0 to 108.0 in bulk drug samples. Isosulfan blue sample solution and mobile phase were found to be stable for at least 72 h. The proposed method was found to be suitable and accurate for the quantitative determination of impurity-A, impurity-B, impurity-C and other unknown impurities in isosulfan blue drug substances.Conclusion: A new, simple and rapid method has been developed and validated for separation and determination of impurity-A, impurity-B, impurity-C and unknown impurities of isosulfan blue by the reverse-phase liquid chromatographic method. Analytical method was developed and validated as per ICH guidelines.The developed method can be used for the quantitative determination of impurity A, impurity B, impurity C and unknown impurities in isosulfan blue drug substances in pharmaceutical industry.

Emotional design for smart product-service system: A case study on smart beds

The aim of study was to develop a suitable analytical method for simultaneous estimation of levodopa, carbidopa and 3-O-methyl dopa in rat plasma. Chromatographic separation of plasma samples was achieved using a reverse-phase C18 column. The mobile phase used consisted of a mixture of methanol and phosphate buffer (10 mM, pH 3.50) in the ratio of 90:10 v/v. All analytes were estimated by electrochemical detection at +800 mV. The developed method has been validated as per the standard guidelines. Precision study results were found to be satisfactory, with percentage relative standard deviation for repeatability and intermediate precision <3.96 and 6.56%, respectively, for all analytes detected in rat plasma. The developed method in rat plasma was found to be simple, rapid, accurate, precise and specific. The proposed method has been successfully applied for analysis of rat plasma samples obtained during an oral pharmacokinetic study of sustained release pellets of levodopa and carbidopa in rats. Copyright © 2016 John Wiley & Sons, Ltd.